Forecasting and simulation

Forecasting the likely evolution of consumer demand plays an important part in supply chain management and planning future marketing strategies.

Accuracy depends critically upon precise quantification of past consumer behaviour, either in the form of sales extrapolation techniques or more complex methods such as dynamic econometric modelling.

Marketscience Consulting specialises in advanced forecasting techniques, ranging from univariate time series methods through to dynamic econometric modelling incorporating all relevant business drivers.

Supply chain management

Supply chain management relies heavily on accurate consumer demand forecasting. This can benefit both manufacturer and retailer alike. On the one hand, responsibility for inventory control often lies with the manufacturer. However, under-supply is often penalised leading to an increased incentive to overstock at considerable cost. On the other hand, successful retailing requires that stores are stocked with the right products at the right price. Consequently, techniques for correctly predicting consumer demand are critical to both parties.

Univariate time series models

Univariate forecasting methods are based on the notion that any time series of data can be broken down into core components such as trend, cycle, season and random error. Once estimated, these components are then used to extrapolate historical sales behaviour over subsequent time periods.

Time series decomposition methods fall into two broad camps: Autoregressive Integrated Moving Average (ARIMA) based techniques and Unobserved Component Modelling (UCM). ARIMA methods are more popularised, yet the time series components are often difficult to identify. The UCM approach, on the other hand, is more flexible since components are explicitly identified yet the model form retains the benefits of an implicit ARIMA structure.

Scenario planning and simulation

Although quick and powerful, univariate methods lack any behavioural content. That is, they predict performance solely on the basis of the sales series’ own past yet provide no understanding of the economic drivers of the business. Consequently, they cannot be used for simulating the impact of economic change or planned marketing strategies.

Dynamic econometric models

More advanced forecasting methods involve a combination of time series structures and demand drivers. Such techniques are known as transfer functions and form the basis of dynamic econometric modelling. This approach provides accurate prediction and a more detailed understanding of the business impact of marketing strategies. As such, they are ideal for both supply chain forecasting and scenario planning. Marketscience Consulting dynamic marketing mix models are a good example of this technique. More